An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

Abstract

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction (NWP) caused by errors in initial conditions (ICs). The traditional Singular Vector (SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm, energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to (i) improve the relationship between the ensemble spread and the root-mean-square error and (ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short- to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

摘 要

集合预报是解决数值天气预报(NWP)中初值(ICs)不确定性的核心技术之一. 传统的奇异向量(SV)初值扰动方法能体现全球集合预报模式中天气尺度的初始不确定性, 然而对中尺度初值不确定性描述不足. 针对这一问题, 我们提出了一种适用于全球集合预报(GEPS)的多尺度 SV 初值扰动方法来定量描述初始条件中的多尺度不确定性. 多尺度SV初值扰动方法的核心是在切线性伴随模式中基于不同时空尺度的设置计算多组SVs, 再利用新设计的一种考虑幅值的高斯随机采样方法将所有SVs线性组合成适用于全球集合预报的初始扰动, 体现目标区域内中尺度到天气尺度快速增长的不稳定扰动结构. 本文将多尺度SV初始扰动方法应用于CMA-GEPS全球集合预报模式中, 开展不同季节的集合预报批量试验, 分析了多尺度 SV 的能量模、动能谱和空间结构特征以及对全球集合预报性能的影响. 结果表明, 相较传统的单一尺度SV方法, 多尺度SV初始扰动能有效捕捉ICs中更多的中尺度不确定性. 同时, 多尺度 SV 初始扰动能体现目标区域内大气初始条件中斜压不稳定扰动及其演变特点. 最后, 多尺度 SV 可以(i)有效改善全球集合预报中离散度与均方根误差之间的平衡关系; (ii) 提升对于预报后期大气环流形势和短、 中期24h累计降水的概率预报技巧. 研究为设计和发展适用于全球集合预报系统的多尺度 SV 初值扰动方法提供了科学依据和应用基础.